Background: The cytologic qualities of noninvasive follicular neoplasm with papillary-like nuclear features mimic papillary thyroid carcinoma (PTC) on fine-needle aspiration, leading to difficulty in distinguishing the 2 preoperatively. The aim of this study was to determine the impact of reclassification of noninvasive follicular neoplasm with papillary-like nuclear features at our practice.
Methods: We searched 1,046 patient charts for those cases with preoperative cytology and subsequent follicular-variant papillary thyroid carcinoma diagnosis on resection. Endocrine pathologists reviewed the cases to determine the reclassification of noninvasive follicular neoplasm with papillary-like nuclear features.
Results: Sixty (6%) follicular-variant papillary thyroid carcinomas were identified, 4% (44) in the index nodule. Of the 44 patients, 84% (37) met the criteria for evaluation. Of these, 46% (17) were noninvasive follicular neoplasm with papillary-like nuclear features. After reclassification of noninvasive follicular-variant papillary thyroid carcinoma to noninvasive follicular neoplasm with papillary-like nuclear features, the overall cancer rate changed from 31% to 29%. Malignancy rates across Bethesda cytologic categories changed as follows: benign (n = 419) from 3.5% to 3.3%; atypia of undetermined significance/follicular lesion of undetermined significance (n = 240) from 17% to 15%; suspicious for follicular neoplasm (n = 104) from 23% to 21%; suspicious for malignancy (n = 85) from 68% to 60%, and malignant (n = 198) from 93% to 92%.
Conclusion: Reclassification of noninvasive follicular neoplasm with papillary-like nuclear features led to a small decrease in the overall malignancy rate. The most affected Bethesda category was suspicious for malignancy. Because the majority of noninvasive follicular neoplasm with papillary-like nuclear features will be indeterminate lesions by cytology/molecular testing, thyroidectomy will remain a common treatment modality. Noninvasive follicular neoplasm with papillary-like nuclear features classification will primarily affect decision making to avoid excessive treatment/monitoring.
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